Data catalogs have quickly become a core component of modern data management. Organizations with successful data catalog implementations see remarkable changes in the speed and quality of data analysis, and in the engagement and enthusiasm of people who need to perform data analysis. By contrast, organizations without a data catalog often have these questions: What is this basically? Why do we need a data catalog? What does this catalog do? These are all absolutely relevant and good questions and a logical place to start your data cataloging journey.
A collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses.
This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata.
These have become the standard for metadata management in the age of big data. The metadata that we need today is more expansive than metadata. A data catalog focuses first on datasets (the inventory of available data) and connects those datasets with rich information to inform people who work with data.
What Does a Data Catalog Do?
A catalog includes many features and functions that all depend on the core capability of cataloging data—collecting the metadata that identifies and describes the inventory of shareable data. It is impractical to attempt cataloging as a manual effort. Automated discovery of datasets, both for initial catalog build and ongoing discovery of new datasets is essential. Use of AI and machine learning for metadata collection, semantic inference, and tagging, is important to get maximum value from automation and minimize manual effort. For more information you can check Metadata catalog
Benefits of a Data Catalog
- Improved data efficiency
- Improved data context
- Reduced risk of error
- Improved data analysis
The data management benefits of a data catalog become apparent by reflecting on the value of metadata and the capabilities that are created with comprehensive metadata. The greatest value, however, is often seen in the impact on analysis activities. We work in an age of self-service analytics. IT organizations can’t provide all of the data needed by the ever-increasing numbers of people who analyze data. But today’s business and data analysts are often working blind, without visibility into the datasets that exist, the contents of those datasets, and the quality and usefulness of each.
Conclusion
Managing data in the age of big data, data lakes, and self-service is challenging. Data catalogs help to step up to those challenges. Active data curation is a core element of data catalog success and a critical practice for modern data
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